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Systems design of national system for management of scientific and technological information, including prototype subsystem for handling earth resources data
Ferroacoustic memory storage description and thin film testing for improved speed and data density
Verification of organism survival /Artemia cysts/ at temperatures near absolute zero, showing that information necessary for living system specification is stored in atom configuration
The control of electron transfer rates is discussed and a molecular shift register memory at the molecular level is described. The memory elements are made up of molecules which can exist in either an oxidized or reduced state and the bits can be shifted between the cells with photoinduced electron transfer reactions. The device integrates designed molecules onto a VLSI substrate. A control structure to modify the flow of information along a shift register is indicated schematically.
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Feasibility of achieving optical densities in magnetic data storage
Curie point writing on MnBi thin films using pulsed high intensity ruby laser, flash lamp, focusing cavity and pinhole aperture
There are no author-identified significant results in this report.
Electron-optical methods applied to information storage for direct retrieval from Mars and other planetary missions
Short term storage of visual information and property of memory read-out
An apparatus is disclosed for reading and/or writing information or to from an optical recording medium having a plurality of information storage layers. The apparatus includes a dynamic holographic optical element configured to focus light on the optical recording medium. a control circuit arranged to supply a drive signal to the holographic optical element, and a storage device in communication with the control circuit and storing at least a first drive signal and a second drive signal. The holographic optical element focusses light on a first one of the plurality of information storage layers when driven by the first drive signal on a second one of the plurality of information storage layers when driven by the second drive signal. An optical switch is also disclosed for connecting at least one light source in a source array to at least one light receiver in a receiver array. The switch includes a dynamic holographic optical element configured to receive light from the source array and to transmit light to the receiver array, a control circuit arranged to supply a drive signal to the holographic optical element, and a storage device in communication with the control circuit and storing at least a first drive signal and a second drive signal. The holographic optical element connects a first light source in the source array to a first light receiver in the receiver array when driven by the first drive signal and the holographic optical element connects the first light source with the first light receiver and a second light receiver when driven by the second drive signal.
The collection and dissemination of thermal energy storage (TES) system technology for the pulp and paper industry with the intent of reducing fossil fuel usage is discussed. The study plan is described and a description presented of example TES systems.
A scheme for nonvolatile associative electronic memory storage with high information storage density is proposed which is based on neural network models and which uses a matrix of two-terminal passive interconnections (synapses). It is noted that the massive parallelism in the architecture would require the ON state of a synaptic connection to be unusually weak (highly resistive). Memory switching using a-Si:H along with ballast resistors patterned from amorphous Ge-metal alloys is investigated for a binary programmable read only memory matrix. The fabrication of a 1600 synapse test array of uniform connection strengths and a-Si:H switching elements is discussed.
Alpha iron particles used for magnetic recording are prepared through a series of dehydration and reduction steps of alpha-Fe2O3-H2O resulting in acicular, polycrystalline, body centered cubic (bcc) alpha-Fe particles that are single magnetic domains. Since fine iron particles are pyrophoric by nature, stabilization processes had to be developed in order for iron particles to be considered as a viable recording medium for long term archival (i.e., 25+ years) information storage. The primary means of establishing stability is through passivation or controlled oxidation of the iron particle's surface. Since iron particles used for magnetic recording are small, additional oxidation has a direct impact on performance especially where archival storage of recorded information for long periods of time is important. Further stabilization chemistry/processes had to be developed to guarantee that iron particles could be considered as a viable long term recording medium. In an effort to retard the diffusion of iron ions through the oxide layer, other elements such as silicon, aluminum, and chromium have been added to the base iron to promote more dense scale formation or to alleviate some of the non-stoichiometric behavior of the oxide or both. The presence of water vapor has been shown to disrupt the passive layer, subsequently increasing the oxidation rate of the iron. A study was undertaken to examine the degradation in magnetic properties as a function of both temperature and humidity on silicon-containing iron particles between 50-120 deg C and 3-89 percent relative humidity. The methodology to which experimental data was collected and analyzed leading to predictive capability is discussed.
Overview Storage capacity is an essential reservoir metric that is directly linked to various water management and energy objectives. Accurate reporting and tracking of change in storage over time is crucial for the safe and reliable operation of the associated dam. While storage information is available for many reservoirs through the National Inventory of Dams, additional details, e.g. water elevation levels as well as changes over time are not included. This dataset contains reservoir storage capacities based on conducted surveys in CONUS. To represent changes in a reservoir’s storage over time, the storage capacity as determined by the first and last conducted survey is listed. The level of detail of surveys can vary greatly and improved with technological advancements. Therefore, the type of survey and year when it was conducted is noted. To ensure a fair comparison of storage capacities, the water elevation level along with the corresponding operation of the dam is reported. Structural changes, e.g. heightening of a dam will have an influence on the storage capacity and are therefore also mentioned. A total of 739 different reservoir storage capacity comparisons are listed, with some reservoirs represented more than once (storage capacity comparison at different water elevation levels). Methodology Data were acquired from USBR reservoir survey reports, TWDB lake survey reports and elevation-area-capacity tables, the RSI Web Portal and the NID (USACE, 2024). Initial storage capacity along with year and type of survey record is compared to the most recent reported storage capacity, survey type and year. Comparison elevation in feet as well as comparison elevation type were either extracted from survey reports (USBR, TWDB) or the Web Portal (RSI) and in some cases cross-referenced with data from other sources (Water Management Data, USACE, Water Data for Texas, TWDB).
Neurophysiological cerebral correlation model of information transactions, and information storage
Machine learning inspired potentials continue to improve the ability for predicting structures of materials. However, many challenges still exist, particularly when calculating structures of disordered systems. These challenges are primarily due to the rapidly increasing dimensionality of the feature-vector space which in most machine-learning algorithms is dependent on the size of the structure. In this article, we present a feature-engineered approach that establishes a set of principles for representing potentials of physical structures (crystals, molecules, and clusters) in a feature space rather than a physically motivated space. Our goal in this work is to define guiding principles that optimize information storage of the physical parameters within the feature representations. In this manner, we focus on keeping the dimensionality of the feature space independent of the number of atoms in the structure. Our Structural Information Filtered Features (SIFF) potential represents structures by utilizing a feature vector of low-correlated descriptors, which correspondingly maximizes information within the descriptor. We present results of our SIFF potential on datasets composed of disordered (carbon and carbon–oxygen) clusters, molecules with C 7 O 2 H 2 stoichiometry in the GDB9-14B dataset, and crystal structures of the form (Al x Ga y In z ) 2 O 3 as proposed in the NOMAD Kaggle competition. Our potential's performance is at least comparable, sometimes significantly more accurate, and often more efficient than other well-known machine-learning potentials for structure prediction. However, primarily, we offer a different perspective on how researchers should consider opportunities in maximizing information storage for features.